# Data Science in Healthcare Semester: Spring | Year offered: 2025 **To register for this course, please complete** [**this form**](https://hms.az1.qualtrics.com/jfe/form/SV_3g6XYzEuDJaqkOq) **by February 12.** ## Course description Data science plays a significant role in many fields, including the applications in healthcare, aided by advancements in artificial intelligence and specifically, in machine learning. By combining vast amounts of data and advancing machine learning algorithms, data science empowers healthcare providers and public health practitioners to improve patient outcomes and optimize healthcare delivery with better data-driven decisions. This nanocourse provides an introduction to the data science tools employed in healthcare and highlights the importance of facilitating collaboration between the fields of healthcare and computer science. Topics that will be covered in this nanocourse include the use cases for predicting patient outcomes, reducing healthcare costs, optimizing healthcare processes and streamlining operations, enhancing personalized treatment plans, and developing early diagnostic tools. ## Course objectives The aim of this nanocourse is to 1) introduce students, researchers, and healthcare providers to the principles and concepts of data science in healthcare, and 2) to explore the stages of designing and developing applications for data-driven clinical decision making and medical use cases. Participants in this nanocourse will gain a fundamental understanding of practical considerations, benefits, challenges, future trends and opportunities for integrating data science tools into medical problems, including personalized patient care, early diagnostics and prevention, and predictive analysis, through lectures and group discussions. The data science tools will include data visualization, regression, decision trees, neural networks, and clustering. The evolving role of healthcare providers and public health practitioners in the era of data science / artificial intelligence will also be addressed in this nanocourse. ## Session dates, times, and location **Session 1:** Participants will be introduced to the basics of data science, such as common terms and definitions, an overview of data science applications in healthcare, and the important relationships between the fields of healthcare and computer science. The reasons why data science tools are needed in medicine, the types of data, the features of several modeling approaches used in healthcare will also be presented. Each of the concepts covered in this session will be explored through example practices of data science applications to solve healthcare problems. These examples will be related to predictive analytics in patient care, operational efficiency in healthcare delivery processes, and personalized medicine. **Session 2:** Participants will work on one of the two possible case studies around the applications of data science in healthcare based on their interests in groups. The case studies will mainly focus on predictive analytics in patient care and personalized medicine. Participants are expected to read the case they will select before the session and prepare responses to the questions. For that purpose, they will receive both the case studies with several short-answer questions after completing the first session. They are also expected to be prepared for an interactive group discussion during the session. Upon completing the course, participants will be able to describe, interpret and evaluate the role of data science in healthcare, understand how to apply data science tools to improve patient care and optimize health outcomes, and discover emerging research trends in this field. Participants will be provided with resources to further explore course content. ## **Time and Location:** *Session 1: February 19, 2025, 1:00-4:00PM, Hybrid, TMEC 446 and Zoom* *Session 2: February 26, 2025, 1:00-4:00PM, In-person, TMEC 446* ## Milestone credit To receive a Milestone, students must attend both sessions in person and complete the case activity (short answer questions and preparation to lead a portion of the case discussion on Day 2). More information about Milestone Credit can be found [here](https://curriculumfellows.hms.harvard.edu/nanocourses). ## Course Team **Course Instructor** Dr. Melike Hazal Can, Postdoctoral Fellow, **Curriculum Advisor** Dr. Deepali Ravel, Lecturer, [Deepali\_Ravel@hms.harvard.edu](mailto:Deepali_Ravel@hms.harvard.edu) ## **Registration** There is no enrollment limit for this course. **To register for this course, please complete** [**this form**](https://hms.az1.qualtrics.com/jfe/form/SV_3g6XYzEuDJaqkOq) **by February 12.** *This nanocourse is part of the Public Health 101 nanocourse series and is sponsored by the HSPH Postdoctoral Association and the Harvard Infectious Diseases Consortium.* See also:- [ Past ](/class-categories/past)